Segmentation of left atrial intracardiac ultrasound images for image guided cardiac ablation therapy

M. E. Rettmann, T. Stephens, D. R. Holmes, C. Linte, D. L. Packer, R. A. Robb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Intracardiac echocardiography (ICE), a technique in which structures of the heart are imaged using a catheter navigated inside the cardiac chambers, is an important imaging technique for guidance in cardiac ablation therapy. Automatic segmentation of these images is valuable for guidance and targeting of treatment sites. In this paper, we describe an approach to segment ICE images by generating an empirical model of blood pool and tissue intensities. Normal, Weibull, Gamma, and Generalized Extreme Value (GEV) distributions are fit to histograms of tissue and blood pool pixels from a series of ICE scans. A total of 40 images from 4 separate studies were evaluated. The model was trained and tested using two approaches. In the first approach, the model was trained on all images from 3 studies and subsequently tested on the 40 images from the 4th study. This procedure was repeated 4 times using a leave-one-out strategy. This is termed the between-subjects approach. In the second approach, the model was trained on 10 randomly selected images from a single study and tested on the remaining 30 images in that study. This is termed the within-subjects approach. For both approaches, the model was used to automatically segment ICE images into blood and tissue regions. Each pixel is classified using the Generalized Liklihood Ratio Test across neighborhood sizes ranging from 1 to 49. Automatic segmentation results were compared against manual segmentations for all images. In the between-subjects approach, the GEV distribution using a neighborhood size of 17 was found to be the most accurate with a misclassification rate of approximately 17%. In the within-subjects approach, the GEV distribution using a neighborhood size of 19 was found to be the most accurate with a misclassification rate of approximately 15%. As expected, the majority of misclassified pixels were located near the boundaries between tissue and blood pool regions for both methods.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2013
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
DOIs
StatePublished - Jun 10 2013
EventMedical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling - Lake Buena Vista, FL, United States
Duration: Feb 12 2013Feb 14 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8671
ISSN (Print)0277-786X

Other

OtherMedical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CityLake Buena Vista, FL
Period2/12/132/14/13

Keywords

  • Image-guided interventions
  • Intra-cardiac ultrasound
  • Left atrium
  • Ultrasound segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Rettmann, M. E., Stephens, T., Holmes, D. R., Linte, C., Packer, D. L., & Robb, R. A. (2013). Segmentation of left atrial intracardiac ultrasound images for image guided cardiac ablation therapy. In Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling [86712D] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8671). https://doi.org/10.1117/12.2008762